Within-host competition causes pathogen molecular evolution and perpetual microbiota dysbiosis
Bibliographic record
Abstract
Pathogens newly invading a host must compete with resident microbiota. This within-host microbial warfare could lead to more severe disease outcomes or constrain the evolution of virulence. By passaging a widespread pathogen (Staphylococcus aureus) and a natural microbiota community across populations of nematode hosts, we show that the pathogen displaced microbiota and reduced species richness, but maintained its virulence across generations. Conversely, pathogen populations and microbiota passaged in isolation caused more host harm relative to their respective no-host controls. For the evolved pathogens, this increase in virulence was partly mediated by enhanced biofilm formation and expression of the global virulence regulator agr. Whole genome sequencing revealed shifts in the mode of selection from directional (on pathogens evolving in isolation) to fluctuating (on pathogens evolving in host microbiota). This approach also revealed that competitive interactions with the microbiota drove early pathogen genomic diversification. Metagenome sequencing of the passaged microbiota shows that evolution in pathogen-infected hosts caused a significant reduction in community stability (dysbiosis), along with restrictions on the co-existence of some species based on nutrient competition. Our study reveals how microbial competition during novel infection could determine the patterns and processes of evolution with major consequences for host health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".